Spatial correlation filter and its application in hyperspectral ground objects recognition

Abstract

ABSTRACT Research on spatial information is a hot topic in hyperspectral remote-sensing image (HSI) exploitation. This paper defines a spatial correlation filter to study the spatial variation characteristics and intensity of regionalized variables. Unlike existing spatial information expression methods based on theoretical model, the novel filter takes multi-scale and multi-directional spatial structure features into account and visualizes them, which is model-free and more comprehensive. Further, SCFB model constructed by the filter banks is proposed for pixel-wise classification of HSI and performs well when replacing the classifier component with multiple machine-learning algorithms. Accurate classification on two classic hyperspectral datasets with approximately 3% training samples and on one large dataset with 0.544% training samples indicate that the model is promising in small samples learning. In particular, supervised linear discriminant analysis (LDA) used in the feature subspace optimization part of the model significantly outperforms the other two spectral strategies. Most notably, the model is not only superior to the traditional machine-learning methods considering spectral information only, but also has advantages in accuracy, the number of learnable kernel parameters and time-consuming compared with equal-layer convolution neural network.